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As a senile chronic, progressive and currently incurable disease, dementia has an enormous impact on society and life quality of the elderly. The development of teleoperation technology has changed the traditional way of care delivery and brought a variety of novel applications for dementia care. In this paper, a telerobotic system is presented which gives the caregivers the capability of assisting dementia elderly remotely. The proposed system is composed of a dual-arm collaborative robot (YuMi) and a wearable motion capture device. The communication architecture is achieved by the robot operation system (ROS). The position-orientation data of the operator's hand are obtained and used to control the YuMi robot. Besides, a path-constrained mapping method is designed for motion trajectory tracking between the robot and the operator in the progress of teleoperation. Meanwhile, corresponding experiments are conducted to verify the performance of the trajectory tracking using the path-constrained mapping method. Results show that the position tracking deviation between the trajectory of the operator and the robot measured by dynamic time warping distance is 1.05 mm at the sampling frequency of 7.5 Hz. Moreover, the practicability of the proposed system was verified by teleoperating the YuMi robot to pick up a medicine bottle and further demonstrated by assisting an elderly woman in picking up a cup remotely. The proposed telerobotic system has potential utility for improving the life quality of dementia elderly and the care effect of their caregivers.

Recently, the collaborative robot (Cobot) has become an emerging subfield in robotics, which significantly expands the applications of robots, such as smart manufacturing, [1] professional service, [2] and health care. [3] Thanks to the development of sensing technology, [4] data analysis, [5] and control science, [6] the adaptability of Cobot to complex unconstructed environments has enhanced hugely. However, in human-robot collaboration (HRC), the further integration of Cobots and human daily life still needs more advanced devices or intelligent systems to assist Cobots to satisfy several essential requirements, such as security assurance, [7] information perception, [8] and emotional communication. [9] The paramount differences between Cobots and traditional robots lie in sensor systems and safety strategies. Examples of sensing systems of Cobots include computer vision, [10] proximity sensing, [9] and tactile interaction. [11] Compared with Cobots which solely obtain information from cameras, Cobots with tactile sensing ability are more capable of cooperating with humans in complex environments, such as places with dim lighting, smoke-filled areas, or visual blind spots. [12] Thus, robot skin plays an essential role in physical human-robot interaction, which includes tactile sensing and buffering capacity. From the perspective of tactile sensing, robot skin to endow host Cobots with tactile sensing function to satisfy the perceptual requirements of robots' adaptation in unstructured and constrained environments has become exceedingly heated research interdisciplinary in robotics. [13] In addition, the tactile sensing function of robot skin also provides more opportunities for Cobots to get control information from the human partner in HRC, which will definitely enhance the safety flexibility and efficiency of HRC. Moreover, tactile sensing of the robot also makes it possible for human and robot emotional interaction through physical touching. Collision detection and buffering are also important functions of robot skin in HRC. There are two ways for robot skin to realize collision detection: One is to use viscoelastic material as raw material of sensors or substrate material of pressure sensors to cushion the collision and detect the peak force of a collision; [14] the other is to use flexible sensors attached to the airbag structure to detect the peak force of a collision. [7,15]

Robots equipped with bionic skins for enhancing the robot perception capability are increasingly deployed in wide applications ranging from healthcare to industry. Artificial intelligence algorithms that can provide bionic skins with efficient signal processing functions further accelerate the development of this trend. Inspired by the somatosensory processing hierarchy of humans, the bioinspired co‐design of a tactile sensor and a deep learning‐based algorithm is proposed herein, simplifying the sensor structure while providing computation‐enhanced tactile sensing performance. The soft piezoresistive sensor, based on the carbon black‐coated polyurethane sponge, offers a continuous sensing area. By utilizing a customized deep neural network (DNN), it can detect external tactile stimulus spatially continuously. Besides, a novel data augmentation method is developed based on the sensor's hexagonal structure that has a sixfold rotation symmetry. It can significantly enhance the generalization ability of the DNN model by enriching the collected training data with generated pseudo‐data. The functionality of the sensor and the robustness of the proposed data augmentation strategy are verified by precisely recognizing five touch modalities, illustrating a well‐generalized performance, and providing a promising application prospect in human–robot interaction.